Nonparametric long term prediction of stock returns with generated bond yields

Recent empirical approaches in forecasting equity returns or premiums found that dynamic interactions among the stock and bond are relevant for long term pension products. Automatic procedures to upgrade or downgrade risk exposure could potentially improve long term performance for such products. The risk and return of bonds is more easy to predict than the risk and return of stocks. This and the well known stock-bond correlation motivates the inclusion of the current bond yield in a model for the prediction of excess stock returns. Here, we take the actuarial long term view using yearly data, and focus on nonlinear relationships between a set of covariates. We employ fully nonparametric models and apply for estimation a local-linear kernel smoother. Since the current bond yield is not known, it is predicted in a prior step. The structure imposed this way in the final estimation process helps to circumvent the curse of dimensionality and reduces bias in the estimation of excess stock returns. Our validated stock prediction results show that predicted bond returns improve stock prediction significantly

Nonparametric Prediction of Stock Returns Based on Yearly Data: The Long-Term View

One of the most studied questions in economics and finance is whether empirical models can be used to predict equity returns or premiums. In this paper, we take the actuarial long-term view and base our prediction on yearly data from 1872 through 2014. While many authors favor the historical mean or other parametric methods, this article focuses on nonlinear relationships between a set of covariates. A bootstrap test on the true functional form of the conditional expected returns confirms that yearly returns on the S&P500 are predictable. The inclusion of prior knowledge in our nonlinear model shows notable improvement in the prediction of excess stock returns compared to a fully nonparametric model. Statistically, a bias and dimension reduction method is proposed to import more structure in the estimation process as an adequate way to circumvent the curse of dimensionality

Package wsbackfit for Smooth Backfitting Estimation of Generalized Structured Models

A package is introduced that provides the weighted smooth backfitting estimator for a large family of popular semiparametric regression models. This family is known as generalized structured models, comprising, for example, generalized varying coefficient model, generalized additive models, mixtures, potentially including parametric parts. The kernel-based weighted smooth backfitting belongs to the statistically most efficient procedures for this model class. Its asymptotic properties are well-understood thanks to the large body of literature about this estimator. The introduced weights allow for the inclusion of sampling weights, trimming, and efficient estimation under heteroscedasticity. Further options facilitate easy handling of aggregated data, prediction, and the presentation of estimation results. Cross-validation methods are provided which can be used for model and bandwidth selection.

One Sided Crossvalidation for Density Estimation

We introduce one-sided cross-validation to nonparametric kernel density estimation. The method is more stable than classical cross-validation and it has a better overall performance comparable to what we see in plug-in methods. One-sided cross-validation is a more direct date driven method than plugin methods with weaker assumptions of smoothness since it does not require a smooth pilot with consistent second derivatives. Our conclusions for one-sided kernel density cross-validation are similar to the conclusions obtained by Hart and Li (1998) when they introduced one-sided cross-validation in the regression context. An extensive simulation study conms that our one-sided cross-validation clearly outperforms the simple cross validation. We conclude with real data applications

Continuous Chain Ladder: Reformulating and generalizing a classical insurance problem

The single most important number in the accounts of a non-life insurance company is likely to be the estimate of the outlying liabilities. Since non-life insurance is a major part of our financial industry (amounting to up to 5% of BNP in western countries), it is perhaps surprising that mathematical statisticians and experts of operational research (the natural experts of the underlying problem) have left the intellectual work on estimating this number to actuaries. This paper establishes this important problem in a vocabulary accessible to experts of operations research and mathematical statistics and it can be seen as an open invitation to these two important groups of scholars to join this research. The paper introduces a number of new methodologies and approaches to estimating outstanding liabilities in non-life insurance. In particular it reformulates the classical actuarial technique as a histogram type of approach and improves this classical technique by replacing this histogram by a kernel smoother

Predictive Factors for and Complications of Bronchiectasis in Common Variable Immunodeficiency Disorders

Bronchiectasis is a frequent complication of common variable immunodeficiency disorders (CVID). In a cohort of patients with CVID, we sought to identify predictors of bronchiectasis. Secondly, we sought to describe the impact of bronchiectasis on lung function, infection risk, and quality of life. We conducted an observational cohort study of 110 patients with CVID and an available pulmonary computed tomography scan. The prevalence of bronchiectasis was 53%, with most of these patients (54%) having mild disease. Patients with bronchiectasis had lower median serum immunoglobulin (Ig) concentrations, especially long-term IgM (0 vs 0.25 g/l; p < 0.01) and pre-treatment IgG (1.3 vs 3.7 g/l; p < 0.01). CVID patients with bronchiectasis had worse forced expiratory volume in one second (2.10 vs 2.99 l; p < 0.01) and an annual decline in forced expiratory volume in one second of 25 ml/year (vs 8 ml/year in patients without bronchiectasis; p = 0.01). Patients with bronchiectasis also reported more annual respiratory tract infections (1.77 vs 1.25 infections/year, p = 0.04) and a poorer quality of life (26 vs 14 points in the St George's Respiratory Questionnaire; p = 0.02). Low serum immunoglobulin M concentration identifies patients at risk for bronchiectasis in CVID and may play a role in pathogenesis. Bronchiectasis is relevant because it is associated with frequent respiratory tract infections, poorer lung function, a greater rate of lung function decline, and a lower quality of life

All-optical dc nanotesla magnetometry using silicon vacancy fine structure in isotopically purified silicon carbide

We uncover the fine structure of a silicon vacancy in isotopically purified silicon carbide (4H-$^{28}$SiC) and find extra terms in the spin Hamiltonian, originated from the trigonal pyramidal symmetry of this spin-3/2 color center. These terms give rise to additional spin transitions, which are otherwise forbidden, and lead to a level anticrossing in an external magnetic field. We observe a sharp variation of the photoluminescence intensity in the vicinity of this level anticrossing, which can be used for a purely all-optical sensing of the magnetic field. We achieve dc magnetic field sensitivity of 87 nT Hz$^{-1/2}$ within a volume of $3 \times 10^{-7}$ mm$^{3}$ at room temperature and demonstrate that this contactless method is robust at high temperatures up to at least 500 K. As our approach does not require application of radiofrequency fields, it is scalable to much larger volumes. For an optimized light-trapping waveguide of 3 mm$^{3}$ the projection noise limit is below 100 fT Hz$^{-1/2}$.Comment: 12 pages, 6 figures; additional experimental data and an extended theoretical analysis are added in the second versio